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Meta Data Services

About: Meta Data Services is a research topic. Over the lifetime, 2564 publications have been published within this topic receiving 40102 citations.


Papers
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Journal ArticleDOI
01 Jan 2014
TL;DR: This study focuses on how description of methods is supported by examining current metadata schemes for scientific research data, indicating varying degrees of support and guidance for methods description but with the potential for more comprehensive elements on documenting the data production process to be integrated and adopted.
Abstract: Metadata is an essential component to making datasets more accessible by others and facilitating meaningful interpretation and use. In particular, data users expect information on the methods or those processes undertaken to generate data to be available for review and data quality assessment. This study focuses on how description of methods is supported by examining current metadata schemes for scientific research data. Analysis of these schemes indicates varying degrees of support and guidance for methods description but with the potential for more comprehensive elements on documenting the data production process to be integrated and adopted. This preliminary investigation concludes with next steps toward a better understanding of metadata needs for research data and curation support of long-term data sharing and reuse.

10 citations

Patent
Matthew Carroll1
30 Jun 2007
TL;DR: In this paper, a set of OLAP tools are combined with an analysis services server to deduce descriptive metadata from data contained in a column of a relational table and associated existing metadata (e.g., column data type and/or column name).
Abstract: An arrangement for deducing descriptive metadata from data contained in a column of a relational table and associated existing metadata (e.g., that which identifies column data type and/or column name) is provided by a metadata deduction engine in a set of OLAP tools which operates in conjunction with an analysis services server. The metadata deduction engine applies one or more criteria that are configured to evaluate column data in order to deduce metadata that provides additional contextual meaning to the column data beyond that given by the existing metadata. The metadata deduction engine maps the column data to a metadata tag that is passed to the analysis services server to enable it to create an OLAP cube using the deduced metadata.

10 citations

Journal ArticleDOI
TL;DR: This paper attempts to present a brief about old and new metadata formats such as Dublin Core, Text Encoding Initiative, Encoded Archival Description, Government (Global) Information Locator Service, Platform for Internet Content Selection, Resource Description Framework, Meta Content Framework, Summary Object Interchange Format, Digital Object Identifier, Serial Item and Contribution Identifier and Learning Object Metadata.
Abstract: Metadata is data about data, a term that got wide prominence to denote cataloguing and representation of digital information resources. This paper attempts to present a brief about old and new metadata formats such as Dublin Core, Text Encoding Initiative, Encoded Archival Description, Government (Global) Information Locator Service, Platform for Internet Content Selection, Resource Description Framework, Meta Content Framework, Summary Object Interchange Format, Digital Object Identifier, Serial Item and Contribution Identifier, Uniform Resource Characteristics, and Learning Object Metadata. As electronic information resources are rising and digital library initiatives are getting wide acceptance, knowledge of metadata formats will help our library professionals in adapting their skills in cataloguing, classification, subject heading, key wording, and indexing for better inventory and exhaustive usage of electronic information. http://dx.doi.org/10.14429/dbit.24.4.3629

10 citations

Proceedings ArticleDOI
14 Mar 2016
TL;DR: A collection of a large number of user responses regarding identification of spreadsheet metadata from participants of a MOOC is described, to understand how users identify metadata in spreadsheets, and to evaluate two existing approaches of automatic metadata extraction from spreadsheets.
Abstract: Spreadsheets are popular end-user computing applicationsand one reason behind their popularity is that theyoffer a large degree of freedom to their users regarding theway they can structure their data. However, this flexibilityalso makes spreadsheets difficult to understand. Textual documentationcan address this issue, yet for supporting automaticgeneration of textual documentation, an important pre-requisiteis to extract metadata inside spreadsheets. It is a challengethough, to distinguish between data and metadata due to thelack of universally accepted structural patterns in spreadsheets. Two existing approaches for automatic extraction of spreadsheetmetadata were not evaluated on large datasets consisting ofuser inputs. Hence in this paper, we describe the collectionof a large number of user responses regarding identificationof spreadsheet metadata from participants of a MOOC. Wedescribe the use of this large dataset to understand how usersidentify metadata in spreadsheets, and to evaluate two existingapproaches of automatic metadata extraction from spreadsheets. The results provide us with directions to follow in order toimprove metadata extraction approaches, obtained from insightsabout user perception of metadata. We also understand what typeof spreadsheet patterns the existing approaches perform well andon what type poorly, and thus which problem areas to focus onin order to improve.

10 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202313
202261
20212
20202
20196
20188